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arXiv 2019年

作者： Xie, Guoda Huang, Zhixiang Fang, Ming Sha, Wei E.I. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

—A novel symplectic algorithm is proposed to solve the Maxwell-Schrödinger (M-S) system for investigating light-matter interaction. Using the fourth-order symplectic integration and fourth-order collocated differences, Maxwell-Schrödinger equations are discretized in temporal and spatial domains, respectively. The symplectic finite-difference time-domain (SFDTD) algorithm is developed for accurate and efficient study of coherent interaction between electromagnetic fields and artificial atoms. Particularly, the Dirichlet boundary condition is adopted for modeling the Rabi oscillation problems under the semi-classical framework. To implement the Dirichlet boundary condition, image theory is introduced, tailored to the high-order collocated differences. For validating the proposed SFDTD algorithm, three-dimensional numerical studies of the population inversion in the Rabi oscillation are presented. Numerical results show that the proposed high-order SFDTD(4,4) algorithm exhibits better numerical performance than the conventional FDTD(2,2) approach at the aspects of accuracy and efficiency for the long-term simulation. The proposed algorithm opens up a promising way towards a high-accurate energy-conservation modeling and simulation of complex dynamics in nanoscale light-matter interaction. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite difference time domain method

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Overlapping Community Detection Based on Influence and Seeds Extension

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese Institute of Electronics. All right reserved.

关键词： Population dynamics

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Deep Adaptive Fusion Network for High Performance RGBT Tracking

Deep Adaptive Fusion Network for High Performance RGBT Track...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Yuan Gao Chenglong Li Yabin Zhu Jin Tang Tao He Futian Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China Anhui COWAROBOT CO. Ltd Wuhu China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Due to the complementarity of RGB and thermal data, RGBT tracking has received more and more attention in recent years because it can effectively solve the degradation of tracking performance in dark environments and bad weather conditions. How to effectively fuse the information from RGB and thermal modality is the key to give full play to their complementarities for effective RGBT tracking. In this paper, we propose a high performance RGBT tracking framework based on a novel deep adaptive fusion network, named DAFNet. Our DAFNet consists of a recursive fusion chain that could adaptively integrate all layer features in an end-to-end manner. Due to simple yet effective operations in DAFNet, our tracker is able to reach the near-real-time speed. Comparing with the state-of-the-art trackers on two public datasets, our DAFNet tracker achieves the outstanding performance and yields a new state-of-the-art in RGBT tracking.

关键词： Feature extraction Target tracking Robustness Fuses Convolution Adaptive systems

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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of systems Architecture volume 118 (2021) 102212

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Journal of systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

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Image Dehazing Network Based on Dilated Convolution Feature Extraction

Image Dehazing Network Based on Dilated Convolution Feature ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics

作者： Ting Feng Zuoyong Li Chuansheng Wang Xinwei Chen Jiawei Wu Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China College of Mathematics and Computer Science Fuzhou University Fuzhou China

Hazy weather brings a lot of inconvenience to peoples lives, such as transportation. Therefore, image dehazing is still an important focus point. To achieve image dehazing, we proposed a Deep Dilated Residual Haze Network (DDRHNet) based on self-encoder and self-decoder. The proposed mode is an end-to-end supervised method, which directly estimates image dehazing result instead of estimating the atmospheric light and the transmission in the unsupervised and classical atmospheric scattering model. The DDRHNet method obtains image dehazed result by finding the difference between the input hazy image and the output of haze-free image. Experimental results on an open image dehazing dataset called SOTS demonstrate the superiority of the proposed DDRHNet network.

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Interpretable Saliency Map for Deep Reinforcement Learning

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Hong Zheng Yinglong Dai Fumin Yu Yuezhen Hu School of Physics and Electronics Hunan Normal University Changsha 410081 China College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.

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Modeling semantic relationship in multi-turn conversations with hierarchical latent variables

arXiv

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arXiv 2019年

作者： Shen, Lei Feng, Yang Zhan, Haolan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It's practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence and diversity compared to baseline methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

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Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： Electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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Dynamic maintenance of decision rules for decision attribute values’ changing 25th

Dynamic maintenance of decision rules for decision attribute...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Wang, Yingyao Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency. © Springer Nature Switzerland AG 2018.

关键词： Rough set theory

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